Abstract A040: CT Radiomics to predict early recurrence of margin-negative resectable pancreatic ductal adenocarcinoma
Authors: Anwesha Dhara, Liana Langdon-Embry, Michael I. D’Angelica, T. Peter Kingham, Natally Horvat, William R. Jarnagin, Alice C. Wei, Jayasree Chakraborty
Journal: Cancer Research (2022)

Abstract Radiographic CT/MRI staging is pivotal to accurate therapeutic decision-making, and guides the surgical resectability of pancreatic ductal adenocarcinoma (PDAC). The extent of vascular involvement of the lesion as seen through radiographic imaging, which differentiates the regional versus locally advanced disease, and therefore upfront resectability. Following standard criteria, only about 20% of PDAC patients qualify for an upfront surgery but half of them recur within a year, despite the complete removal (margin-negative R0 resection) of the primary tumor It underscores the necessity for a better deciphering of the CT/MRI-based clinical imaging of PDAC using radiomics—the process of extracting high dimensional quantitative imaging information from radiographic images. We have previously described a prospective cohort of resected PDAC (n=161) at Memorial Sloan Kettering Cancer Center for preoperative CT angiography collected between 2009 and 2012. We demonstrated that CT texture features are associated with overall survival. Utilizing the same cohort of resected PDAC, the current study aims to develop a much deeper case-controlled algorithm to further identify the texture features are predictive of early recurrence for margin-negative PDAC patients. We selected all patients following these criteria: margin negative (R0), Gemcitabine/nab-paclitaxel adjuvant chemotherapy (excluding neoadjuvant), clinical-stage IIB, moderate tumor differentiation by histopathology, and no lymph node and distant metastasis. Based on these criteria, a total of 27 patients were selected, where 16 patients had recurred within a year of surgery (high-risk group) and the remaining 11 patients did not recur in 2-years (low-risk group). We focused on Gray-level co-occurrence (GLCM) based on Haralick’s features. GLCM is most extensively used to characterize the texture of a tumor via encoding the spatial distribution of voxels—neighboring pixels of an image. A total of 18 GLCM features were extracted from manually segmented CT of PDAC primary tumors. Univariate analysis with Wilcoxon-rank-sum test (p&amp;lt;0.05) revealed 4 features –energy (GLCM1) and entropy (GLCM9, GLCM17, and GLCM18) based were associated with early recurrence. Energy, or angular speed moment, is a measure of local variation in an image; higher energy represents homogeneity between neighboring pixels. On the contrary, entropy characterizes randomness within an image; higher entropy represents more heterogeneity. The energy feature GLCM1 was significantly lower in high-risk group, whereas the entropy features (GLCM 9, 17, and 18) were significantly higher in high-risk group compared to the low-risk group – suggesting the relationship of tumor heterogeneity with early recurrence. While proposing these GLCM features as the measure of tumor heterogeneity and early recurrence, we are validating them on a larger independent cohort, estimating their benchmark measures of predictive ability. The long-term goal is to develop these as radiomic biomarkers for better guiding the therapeutic decision-making of PDAC. Citation Format: Anwesha Dhara, Liana Langdon-Embry, Michael I. D’Angelica, T. Peter Kingham, Natally Horvat, William R. Jarnagin, Alice C. Wei, Jayasree Chakraborty. CT Radiomics to predict early recurrence of margin-negative resectable pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr A040.